Racial Gradients of Ambient Air Pollution Exposure in Hamilton, Canada
Bibliographic record
Abstract
Environmental justice research in the United States has coalesced around the notion that visible-minority status, along with socioeconomic position (SEP), conditions exposure to environmental health hazards. In the context of long-standing debates over Canada–USA urban differences, we address the question of whether racial gradients exist in air pollution across Hamilton, Canada. Monitored air quality data are spatially interpolated with a kriging algorithm. These interpolated exposures are statistically correlated with 1996 data at the census tract scale, with the aid of multivariate and spatial techniques. The proportion of Latin-Americans in a census tract is positively associated with pollution exposure, even after control for many SEP variables. In contrast, Asian-Canadians are negatively associated with air pollution, and Black-Canadians show no clear correlation at all. Thus, the faces of environmental racism in Canada seem more varied and nuanced than in the USA. Given the immigrant basis of visible minorities in Canada, we argue that Hamilton (and the Canadian city generally) may represent new dimensions of environmental racism driven by economic status at time of entry. In drawing on similar findings in the USA and the United Kingdom, the authors conclude that environmental racism appears present in all jurisdictions, but that the nature and extent of disproportionate exposure differ between countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".